1. Field of the Invention
The present invention relates generally to noninvasive tissue analyte determination. More particularly, the invention relates to methods and apparatus for characterizing physiological and chemical properties of an irradiated tissue sample by extracting spectral features reflecting optical properties of key tissue constituents. Subsequently, based on such spectral features, noninvasive glucose measurements that are biased by physiological changes in tissue are compensated. Alternatively, glucose is measured indirectly based on natural physiological response of tissue to shifts in glucose concentration.
2. Description of Related Art
Noninvasive Measurement of Glucose
Diabetes is a leading cause of death and disability worldwide and afflicts an estimated 16 million Americans. Complications of diabetes include heart and kidney disease, blindness, nerve damage and high blood pressure with the estimated total cost to United States economy alone exceeding $90 billion per year [Diabetes Statistics, Publication No. 98-3926, National Institutes of Health, Bethesda Md. (November 1997)]. Long-term clinical studies show that the onset of complications can be significantly reduced through proper control of blood glucose levels [The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Eng J of Med. 329:977-86 (1993)]. A vital element of diabetes management is the self-monitoring of blood glucose levels by diabetics in the home environment. A significant disadvantage of current monitoring techniques is that they discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis. Therefore, new methods for self-monitoring of blood glucose levels are required to improve the prospects for more rigorous control of blood glucose in diabetic patients.
Numerous approaches have been explored for measuring blood glucose levels, ranging from invasive methods such as microdialysis to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. To date, no noninvasive techniques for the self-monitoring of blood glucose have been certified.
One method, near-infrared spectroscopy involves the illumination of a spot on the body with near-infrared electromagnetic radiation (light in the wavelength range 750-2500 nm). The light is partially absorbed and scattered, according to its interaction with the constituents of the tissue prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue including water, fat, protein and glucose.
Previously reported methods for the noninvasive measurement of glucose through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose as represented in the targeted tissue volume. The tissue volume is the portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The signal due to the absorption of glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements and associated reference blood glucose values (the calibration set) based on an analysis of capillary (fingertip) or venous blood.
Near-infrared spectroscopy has been demonstrated in specific studies to represent a feasible and promising approach to the noninvasive prediction of blood glucose levels. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin Chem, 38:1618-22 (1992) reports three different instrument configurations for measuring diffuse transmittance through the finger in the 600-1300 nm range. Meal tolerance tests were used to perturb the glucose levels of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dl. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive blood glucose sensors based on near-infrared spectroscopy, Artif Org, 18:439-47 (1994); H. Heise, R. Marbach, Effect of data pretreatment on the noninvasive blood glucose measurement by diffuse reflectance near-IR spectroscopy, SPIE Proc, 2089:114-5 (1994); R. Marbach, T. Koschinsky, F. Gries, H. Heise, Noninvasive glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip, Appl Spectrosc, 47:875-81 (1993) and R. Marbach, H. Heise, Optical diffuse reflectance accessory for measurements of skin tissue by near-infrared spectroscopy, Applied Optics 34(4): 610-21 (1995) present results through a diffuse reflectance measurement of the oral mucosa in the 1111-1835 nm range with an optimized diffuse reflectance accessory. In vivo experiments were conducted on single diabetics using glucose tolerance tests and on a population of 133 different subjects. The best standard error of prediction reported was 43 mg/dl and was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation.
K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes, Application of near-infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network, Z Phys Chem, 191S:179-190 (1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler, Enhancing calibration models for noninvasive near-infrared spectroscopic blood glucose determinations, Fresenius J Anal Chem 359:78-82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K. Reichelt, Near-infrared diffuse reflection spectroscopy for noninvasive blood-glucose monitoring, LEOS Newsletter 12(2):9-11 (1998); and U. Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer, Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models, Int J Artif Organs, 20:285-290 (1997) recorded spectra in diffuse reflectance over the 800-1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose levels through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks were evaluated on single subjects over single days through cross-validation. Danzer, et al., supra, report an average root mean square prediction error of 36 mg/dl through cross-validation over 31 glucose profiles.
J. Burmeister, M. Arnold, G. Small, Human noninvasive measurement of glucose using near infrared spectroscopy [abstract], Pittcon, New Orleans, La. (1998) collected absorbance spectra through a transmission measurement of the tongue in the 1429-2000 nm range. A study of five diabetic subjects was conducted over a 39-day period with five samples taken per day. Every fifth sample was used for an independent test set and the standard error of prediction for all subjects was greater than 54 mg/dl.
In T. Blank, T. Ruchti, S. Malin, S. Monfre, The use of near-infrared diffuse reflectance for the noninvasive prediction of blood glucose, IEEE Lasers and Electro-Optics Society Newsletter, 13:5 (October 1999), the reported studies demonstrate noninvasive measurement of blood glucose during modified oral glucose tolerance tests over a short time period. The calibration was customized for the individual and tested over a relatively short time period.
In all of these studies, limitations were cited that would affect the acceptance of such a method as a commercial product. These limitations included sensitivity, sampling problems, time lag, calibration bias, long-term reproducibility and instrument noise.
Fundamentally, however, accurate noninvasive estimation of blood glucose is presently limited by the available near-infrared technology, the trace concentration of glucose relative to other constituents and the dynamic nature of the skin and living tissue of the patient (for example, see O. Khalil, Spectroscopic and clinical aspects of noninvasive glucose measurements, Clin Chem, 45:165-77 (1999)). As reported by S. Malin, T. Ruchti, An Intelligent System for Noninvasive Blood Analyte Prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001), the entirety of which is hereby incorporated by reference, chemical, structural and physiological variations occur that produce dramatic and nonlinear changes in the optical properties of the tissue sample [see R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 7:1, pp.13-19 (1981), W. Cheong, S. Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, 26:12, pp.2166-2185, (December 1990), D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, 1888, pp.10-21 (1993), J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, 40, pp.1123-1140 (December 1984), S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, 1:4, pp.418-424 (October 1996), A. Profio, Light transport in tissue, Applied Optics, 28:12), pp. 2216-2222, (June 1989), M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics, IEEE Transactions on Biomedical Engineering, 36:12, pp.1146-1154 (December 1989), and B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12, pp. 2186-2199].
The measurement is further complicated by the heterogeneity of the sample, the multi-layered structure of the skin and the rapid variation related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations and blood analyte levels. This can be further considered through a discussion of the scattering properties of skin.
Tissue Scattering Properties
Skin Structure
The structure and composition of skin varies widely among individuals as well as between different sites and over time on the same individual. Skin consists of a superficial layer known as the stratum corneum, a stratified cellular epidermis, and an underlying dermis of connective tissue. Below the dermis is the subcutaneous fatty layer or adipose tissue. The epidermis, with a thickness of 10-150 μm, together with the stratum corneum provides a barrier to infection and loss of moisture, while the dermis is the thick inner layer that provides mechanical strength and elasticity [F. Ebling, The Normal Skin, Textbook of Dermatology, 2nd ed.; A. Rook; D. Wilkinson, F. Ebling, Eds.; Blackwell Scientific, Oxford, pp 4-24 (1972)]. In humans, the thickness of the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back and averages approximately 1.2 mm over most of the body [S. Wilson, V. Spence, Phys. Med. Biol., 33:894-897 (1988)].
In the dermis, water accounts for approximately 70% percent of the volume. The next most abundant constituent is collagen, a fibrous protein comprising 70-75% of the dry weight of the dermis. Elastin fibers, also a protein, are plentiful though they constitute only a small proportion of the bulk. In addition, the dermis contains a wide variety of structures (e.g., sweat glands, hair follicles and blood vessels) and other cellular constituents [see F. Ebling, supra]. Conversely, the subcutaneous layer (adipose tissue) is by volume approximately 10% water and consists primarily of cells rich in triglycerides (fat). The concentration of glucose varies in each layer according to the water content, the relative sizes of the fluid compartments, the distribution of capillaries and the perfusion of blood. Due to the high concentration of fat, the average concentration of glucose in subcutaneous tissue is significantly lower than that of the dermis.
Optical Properties of Skin
When near-infrared light is delivered to the skin, a percentage of it is reflected, while the remainder penetrates into the skin. The proportion of reflected light, or specular reflectance is typically between 4-7% of the delivered light over the entire spectrum from 250-3000 nm (for a perpendicular angle of incidence) [J. Parrish, R. Anderson, F. Urbach, D. Pitts, UV-A: Biologic Effects of Ultraviolet Radiation with Emphasis on Human Responses to Longwave Ultraviolet, New York, Plenum Press (1978)]. The 93-96% of the incident light that enters the skin is attenuated due to absorption and scattering within the many layers of the skin. These two processes, combined with orientation of the sensors of the spectrometer instrument, determine the tissue volume irradiated by the source and “sampled” through the collection of diffusely reflected light.
Diffuse reflectance or remittance is defined as that fraction of incident optical radiation that is returned from a turbid sample. Alternately, diffuse transmittance is the fraction of incident optical radiation that is transmitted through a turbid sample. Absorption by the various skin constituents mentioned above accounts for the spectral extinction of the light within each layer. Scattering is the only process by which the beam may be returned to contribute to the diffuse reflectance of the skin. Scattering also has a strong influence on the light that is diffusely transmitted through a portion of the skin.
The scattering in tissues is due to discontinuities in the refractive index on the microscopic level, such as the aqueous-lipid membrane interfaces between each tissue compartment or the collagen fibrils within the extracellular matrix [B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12 (December 1990)]. The spatial distribution and intensity of scattered light depends upon the size and shape of the particles relative to the wavelength, and upon the difference in refractive index between the medium and the constituent particles. The scattering of the dermis is dominated by the scattering from collagen fiber bundles in the 2.8 μm diameter range occupying twenty-one percent of the dermal volume, and the refractive index mismatch is 1.38/1.35 [S. Jacques, Origins of tissue optical properties in the UVA, Visible and NIR Regions, Optical Society of America, Topical Meeting, Orlando Fla. (Mar. 18-22, 1996)]. The spectral characteristics of diffuse remittance from tissue result from a complex interplay of the intrinsic absorption and scattering properties of the tissue, the distribution of the heterogeneous scattering components and the geometry of the point(s) of irradiation relative to the point(s) of light detection.
The absorption of light in tissue is primarily due to three fundamental constituents: water, protein and fat. As the main constituent, water dominates the near-infrared absorbance above 1100 nm and is observed through pronounced absorbance bands (for example, see FIG. 3). Protein in its various forms, and in particular collagen, is a strong absorber of light that irradiates the dermis. Near-infrared light that penetrates to subcutaneous tissue is absorbed primarily by fat. In the absence of scattering, the absorbance of near-infrared light due to a particular analyte, A, can be approximated by Beers Law at each wavelength byA=εcl  (1)where ε is the analyte specific absorption coefficient, c is the concentration and/is the pathlength. The overall absorbance at a particular wavelength is the sum of the individual absorbances of each particular analyte given by Beer's Law. The concentration of a particular analyte, such as glucose, can be determined through a multivariate analysis of the absorbance over a multiplicity of wavelengths because ε is unique for each analyte. However, in tissue compartments expected to contain glucose, the concentration of glucose is at least three orders of magnitude less than that of water. Consequently, the signal targeted for detection by reported approaches to near-infrared measurement of glucose (the absorbance due to glucose in the tissue) is expected to be at most three orders of magnitude less than other interfering tissue constituents. Therefore, the near-infrared measurement of glucose requires a high level of sensitivity over a broad wavelength range, and the application of methods of multivariate analysis.
However, the diverse scattering characteristics of the skin (e.g., multiple layers and heterogeneity) cause the light returning from an irradiated sample to vary in a highly nonlinear manner with respect to tissue analytes, in particular, glucose. Simple linear models, such as the Beer's Law have been reported to be invalid for the dermis [R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 77:1, pp. 13-19 (1981).]. Such nonlinear variation is a recognized problem and several reports have disclosed unique methods for compensating for the nonlinearity of the measurement while providing the necessary sensitivity [see S. Malin, et al., supra; E. Thomas, R. Rowe, Methods and Apparatus for Tailoring Spectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000).].
Dynamic Properties of the Skin
While knowledge of and utilization of the optical properties of the skin, high instrument sensitivity and compensation for inherent nonlinearities are all vital for the application of near-infrared spectroscopy to noninvasive blood analyte measurement, an understanding of biological and chemical mechanisms that lead to time dependent changes in the optical properties of skin tissue is equally important and, yet, largely ignored. At a given measurement site, skin tissue is often assumed to be static except for changes in the target analyte and other absorbing species. However, variations in the physiological state of tissue profoundly affect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations are often dominated by fluid compartment equalization through water shifts and are related to hydration levels and changes in blood analyte levels.
Total body water accounts for over 60% of the weight of the average person and is distributed between two major compartments: the extracellular fluid (one-third of total body water) and the intracellular fluid (two-thirds of total body water) [see A. Guyton, J. Hall, Textbook of Medical of Physiology, 9th ed., Philadelphia, W. B. Saunders Company (1996)]. The extracellular fluid in turn is divided into the interstitial fluid (extravascular) and the blood plasma (intravascular). Water permeable lipid membranes separate the compartments and water is transferred rapidly between them through the process of diffusion, in order to equalize the concentrations of water and other analytes across the membrane. The net water flux from one compartment to another constitutes the process of osmosis and the amount of pressure required to prevent osmosis is termed the osmotic pressure. Under static physiological conditions the fluid compartments are at equilibrium. However, during a net fluid gain or loss as a result of water intake or loss, all compartments gain or lose water proportionally and maintain a constant relative volume.
The primary mechanism for distributing substances contained in blood serum that are needed by the tissues, such as water and glucose, is through the process of diffusion. The invention recognizes that Fick's law of diffusion drives the short-term intra-/extra vascular fluid compartment balance. The movement of water and other analytes from intravascular to extravascular compartments occurs rapidly as tremendous numbers of molecules of water and other constituents, in constant thermal motion, diffuse back and forth through the capillary wall. On average, the rate at which water molecules diffuse through the capillary membrane is about eighty times greater than the rate at which the plasma itself flows linearly along the capillary. In the Fick's Law expression, the actual diffusion flux, IOA, is proportional to the concentration gradient, dc/dx between the two compartments and the diffusivity of the molecule, DA according to the equation                               I          OA                =                              -                          D              A                                ⁢                                    -                              ⅆ                c                                                                    ⅆ                x                            ⁢                                                                             ⁢                      .                                              (        2        )            
Short-term increases (or decreases) in blood glucose concentrations lead to an increase (or decrease) in blood osmolality (number of molecules per unit mass of water). Fluid is rapidly re-distributed accordingly and results in a change in the water concentration of each body compartment. For example, the osmotic effect of hyperglycemia is a movement of extravascular water to the intravascular space. Conversely, a decrease in blood glucose concentration leads to a movement of water to extravascular space from the intravascular compartment.
Because the cell membrane is relatively impermeable to most solutes but highly permeable to water, whenever there is a higher concentration of a solute on one side of the cell membrane, water diffuses across the membrane toward the region of higher solute concentration. Large osmotic pressures can develop across the cell membrane with relatively small changes in the concentration of solutes in the extracellular fluid. As a result, relatively small changes in concentration of impermeable solutes in the extracellular fluid, such as glucose, can cause tremendous changes in cell volume.
Long-term fluid compartment balances are influenced by fluid intake, exercise, diet, drug therapy and other physiological factors. The ancillary calibration of glucose to fluid compartment shifts is possible over short-term periods. The calibration of glucose to fluid shifts over longer periods of time requires a bias correction of the analytical signal and the associated blood glucose to compensate for the sources of long-term fluid compartment shifts. It is noted that Fick's Law (equation 2) relates the flux in water concentration to the change in glucose concentration. Thus, this measurement based on first principles only permits the determination of the relative movement of glucose. Bias correction of both the spectroscopic water signal and the associated glucose concentration are required because the initial water concentration is not strictly a function of the associated glucose concentration. Accordingly, without more, it is only feasible to predict relative movement of glucose. Generating an absolute glucose value would require using a paired glucose/water measurement to adjust the time dependent bias in the ancillary fluid shift signal.
The Problem
Re-distribution of water between various tissue compartments alters the optical properties of the tissue through changes in the water concentration, the concentration of other analytes, the refractive indices of various layers, the thickness of tissue layers and the size and distribution of scattering centers. Therefore, the optical properties of the tissue sample are modified in a highly nonlinear and profound manner. In addition, the actual tissue volume sampled (and the effective or average pathlength of light) is varied. Consequently, the spectral measurement varies in a complex manner that is incompatible with current modes of near-infrared detection of glucose. For example, changes in blood glucose concentration will result in water compartment shifts to compensate for the increase or decrease in intravascular osmolality. A change in the distribution of water will lead to a rapid change in the optical properties of the tissue that is correlated to a change in the absorption of glucose.
Several methods are reported to compensate in some part for the dynamic variation of the tissue. For example, several reported methods of noninvasive glucose measurement develop calibration models that are specific to an individual over a short period of time [see Robinson, et al., supra; Burmeister et al., supra; Blank et al., supra; K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August, 1995); and J. Burmeister, In vitro model for human noninvasive blood glucose measurements, Doctoral Dissertation, University of Iowa (December 1997]. This approach avoids modeling the differences between patients and therefore cannot be generalized to more individuals. However, the calibration models have not been tested over long time periods and no means of compensating for variation related to the dynamic water shifts of fluid compartments is reported.
Malin and Ruchti, supra report a method for compensating for variation related to the structure and state of the tissue through an intelligent pattern recognition system capable of determining calibration models that are most appropriate for the patient at the time of measurement. The calibration models are developed from the spectral absorbance of a representative population of patients that have been segregated into groups. The groups or classes are defined on the basis of structural and state similarity such that the variation within a class is small compared to the variation between classes. Classification occurs through extracted features of the tissue absorbance spectrum related to the current patient state and structure. However, the described invention does not use features for directly compensating for physiological changes in the tissue. Further, the direct use of features representing the physiological state of the subject (or subject's measurement site) for noninvasive measurement of glucose was not described.
E. Thomas, R. Rowe, Methods and Apparatus for Tailoring Spectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000) identifies a method for reducing intra-subject variation through the process of mean-centering both the direct and indirect measurements. However, this does not address the key problem related to short-term physiological and chemical changes related to the dynamic nature of the tissue.
No reported method provides a method and apparatus for detecting features that reflect changes in the optical properties of tissue related to physiological properties of the tissue such as the shifting of water between fluid compartments. Second, no reported method utilizes features that reflect the dynamic nature of the tissue to detect conditions unsuitable for near-infrared measurement of blood glucose. Third, no method exists to use these features to compensate glucose measurements for bias caused by physiological changes. Finally, no reported method utilizes fluid compartment shifts as reflected in spectral features related to the optical properties of tissue to indirectly measure glucose. As a result, noninvasive measurement of glucose is limited by the dynamic nature of tissue related to the tissue's physiological response to various conditions and the re-distribution of water among tissue fluid compartments.
In view of the problems left unsolved by the prior art, there exists a need for a method and apparatus to first detect changes in the optical properties of the tissue due to the changing physiology of the subject, specifically changes related to water shifts between tissue compartments. Second, use of these features to determine conditions unsuitable for glucose measurement through near-infrared spectroscopy would be a useful advancement. Finally, it would be a significant advancement to determine a means for either using the features to compensate for the changing optical properties of the tissue or alternately, utilizing the features to measure glucose.